Digital generation of the 3-D pore architecture of isotropic membranes using 2-D cross-sectional scanning electron microscopy images
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arXiv
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| Format: | Preprint |
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2025
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| author | Danalou, Sima Zeinali Chamani, Hooman Rabbani, Arash Lee, Patrick C. Simpers, Jason Hattrick Werber, Jay R |
| author_facet | Danalou, Sima Zeinali Chamani, Hooman Rabbani, Arash Lee, Patrick C. Simpers, Jason Hattrick Werber, Jay R |
| contents | A major limitation of two-dimensional scanning electron microscopy (SEM) in imaging porous membranes is its inability to resolve three-dimensional pore architecture and interconnectivity, which are critical factors governing membrane performance. Although conventional tomographic 3-D reconstruction techniques can address this limitation, they are often expensive, technically challenging, and not widely accessible. We previously introduced a proof-of-concept method for reconstructing a membrane's 3-D pore network from a single 2-D SEM image, yielding statistically equivalent results to those obtained from 3-D tomography. However, this initial approach struggled to replicate the diverse pore geometries commonly observed in real membranes. In this study, we advance the methodology by developing an enhanced reconstruction algorithm that not only maintains essential statistical properties (e.g., pore size distribution), but also accurately reproduces intricate pore morphologies. Applying this technique to a commercial microfiltration membrane, we generated a high-fidelity 3-D reconstruction and derived key membrane properties. Validation with X-ray tomography data revealed excellent agreement in structural metrics, with our SEM-based approach achieving superior resolution in resolving fine pore features. The tool can be readily applied to isotropic porous membrane structures of any pore size, as long as those pores can be visualized by SEM. Further work is needed for 3-D structure generation of anisotropic membranes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06664 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Digital generation of the 3-D pore architecture of isotropic membranes using 2-D cross-sectional scanning electron microscopy images Danalou, Sima Zeinali Chamani, Hooman Rabbani, Arash Lee, Patrick C. Simpers, Jason Hattrick Werber, Jay R Materials Science Image and Video Processing Applied Physics A major limitation of two-dimensional scanning electron microscopy (SEM) in imaging porous membranes is its inability to resolve three-dimensional pore architecture and interconnectivity, which are critical factors governing membrane performance. Although conventional tomographic 3-D reconstruction techniques can address this limitation, they are often expensive, technically challenging, and not widely accessible. We previously introduced a proof-of-concept method for reconstructing a membrane's 3-D pore network from a single 2-D SEM image, yielding statistically equivalent results to those obtained from 3-D tomography. However, this initial approach struggled to replicate the diverse pore geometries commonly observed in real membranes. In this study, we advance the methodology by developing an enhanced reconstruction algorithm that not only maintains essential statistical properties (e.g., pore size distribution), but also accurately reproduces intricate pore morphologies. Applying this technique to a commercial microfiltration membrane, we generated a high-fidelity 3-D reconstruction and derived key membrane properties. Validation with X-ray tomography data revealed excellent agreement in structural metrics, with our SEM-based approach achieving superior resolution in resolving fine pore features. The tool can be readily applied to isotropic porous membrane structures of any pore size, as long as those pores can be visualized by SEM. Further work is needed for 3-D structure generation of anisotropic membranes. |
| title | Digital generation of the 3-D pore architecture of isotropic membranes using 2-D cross-sectional scanning electron microscopy images |
| topic | Materials Science Image and Video Processing Applied Physics |
| url | https://arxiv.org/abs/2508.06664 |